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This study investigates the impacts of the COVID-19 pandemic on urban planning to develop an urban resilience strategy. The vulnerability assessment of Algiers to COVID-19 seeks to develop a correlation between the pandemics' global systemic impacts and Algiers' local urban potential. This research outlines the methodological approach of the Group Analysis Method (GAM), the multi-criteria decision analysis method (MCDA), and the qualitative analytical reading method (SFPO) - Successes, Failures, Potential, and Obstacles. These methods allowed us to build the Restricted Targeted Self-Audit (RTSA) and finally the planning framework. This study yields two distinct types of results: theoretical findings related to the expansion of the urban sustainability model and the empirical findings triggered by the issue of pandemic resilience. The results of this study aim to enhance residents' adaptation to major urban risks through proactive urban planning actions. This will help prevent a critical crisis for the city and its inhabitants in the event of a future pandemic. [ABSTRACT FROM AUTHOR]

This paper introduces the SIS epidemic model into a production-based economy. We study the asset pricing implications of the interaction between COVID-19's shock and mitigation policy for the production economy. The mitigation policy could hinder the consumption, while it will prompt an investment slump. The results indicate some trade-offs between mitigating the transmission of the pandemic and economic output varying with different uncertainty of the pandemic. Notably, the risk premium and growth of capital value present an inverse hump-shape with the infection rate. [ABSTRACT FROM AUTHOR]

Infectious zoonotic disease emergence, through spillover events, is of global concern and has the potential to cause significant harm to society, as recently demonstrated by COVID-19. More than 70% of the 400 infectious diseases that emerged in the past five decades have a zoonotic origin, including all recent pandemics. There have been several approaches used to predict the risk of spillover through some of the known or suspected infectious disease emergence drivers, largely using correlative approaches. Here, we predict the spatial distribution of spillover risk by approximating general transmission through animal and human interactions. These mass action interactions are approximated through the multiplication of the spatial distribution of zoonotic virus diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both virus diversity and human population density are high, it should be noted that this is primarily a conceptual exercise. We compared our spillover risk map to key factors, including the model inputs of zoonotic virus diversity estimate map, human population density map, and the spatial distribution of species richness. Despite the limitations of this approach, this viral spillover map is a step towards developing a more comprehensive spillover risk prediction system to inform global monitoring.

Background: Toxicity on social media, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. Its prevalence intensifies during periods of social crises and unrest, eroding a sense of safety and community. Such toxic environments can adversely impact the mental well-being of those exposed and further deepen societal divisions and polarization. The 2022 mpox outbreak, initially called "monkeypox" but later renamed to reduce stigma and address societal concerns, provides a relevant context for this issue. Objective: In this study, we conducted a comprehensive analysis of the toxic online discourse surrounding the 2022 mpox outbreak. We aimed to dissect its origins, characterize its nature and content, trace its dissemination patterns, and assess its broader societal implications, with the goal of providing insights that can inform strategies to mitigate such toxicity in future crises. Methods: We collected >1.6 million unique tweets and analyzed them with 5 dimensions: context, extent, content, speaker, and intent. Using topic modeling based on bidirectional encoder representations from transformers and social network community clustering, we delineated the toxic dynamics on Twitter. Results: By categorizing topics, we identified 5 high-level categories in the toxic online discourse on Twitter, including disease (20,281/43,521, 46.6%), health policy and health care (8400/43,521, 19.3%), homophobia (10,402/43,521, 23.9%), politics (2611/43,521, 6%), and racism (1784/43,521, 4.1%). Across these categories, users displayed negativity or controversial views on the mpox outbreak, highlighting the escalating political tensions and the weaponization of stigma during this infodemic. Through the toxicity diffusion networks of mentions (17,437 vertices with 3628 clusters), retweets (59,749 vertices with 3015 clusters), and the top users with the highest in-degree centrality, we found that retweets of toxic content were widespread, while influential users rarely engaged with or countered this toxicity through retweets. Conclusions: Our study introduces a comprehensive workflow that combines topical and network analyses to decode emerging social issues during crises. By tracking topical dynamics, we can track the changing popularity of toxic content on the internet, providing a better understanding of societal challenges. Network dynamics highlight key social media influencers and their intentions, suggesting that engaging with these central figures in toxic discourse can improve crisis communication and guide policy making. [ABSTRACT FROM AUTHOR]

There emerges increasing doubt regarding whether the policy composite indicators are applicable to the COVID-19 pandemic. A few early studies demonstrate that the association between some composite indicators of policy preparedness and the risk of COVID-19 is statistically insignificant, and the relation between any composite indicators and recovery process (e.g., vaccination coverage) remains unexplored. To examine the relation between composite indicators and pandemic risk (as well as the vaccination coverage) with robustness, this study applies different policy preparedness indicators by using data from multi-sources. A cross-sectional analysis was performed. Regression analysis is adopted to examine the relation between four policy preparedness indicators (i.e., [1] International Health Regulations core capacity index, [2] Global Health Security Index, [3] epidemic preparedness index, and [4] World Governance Index) and COVID-19-confirmed cases/death/vaccination coverage at different time points. The linear regression is performed, and the spatial distribution of indicators are illustrated. Countries with higher ranking in policy preparedness indexes can experience less severity of pandemic risk (e.g., confirmed cases and mortality) and faster recovery process (e.g., higher vaccination coverage). However, slight disparity in effectiveness exists across different indicators. Results show that the policy preparedness indicators have predictive value of the confirmed cases, mortality, and vaccination coverage of COVID-19 pandemic, given sufficiently long-time span is observed. [ABSTRACT FROM AUTHOR]

Many risks of events with the potential to cause societal collapse either lie far in the future or have a low chance of materializing in any given year. However, optimal mitigation of these long-term destabilizing risks requires state action in the present. We provide a novel framework for understanding why states struggle to formulate a rational response to these risks. In our framework, insights from cognitive psychology, long-term governance and game theory in international relations are integrated. Cognitive biases limit state forecasting accuracy; political incentives and state structures are generally not aligned with tackling the important challenges of the long-term; and global cooperation, a necessary component of mitigation of long-term destabilizing risks, is constrained by suboptimal institutional design in the face of game-theoretical challenges to cooperation. We illustrate this framework by looking at the case of how states fared against epidemic risk in the pre-COVID-19 period. Using 2019 indices of pandemic preparedness, we show that, as a result of the challenges included in our framework, almost all countries failed to take low-cost, high-benefit measures for preventing and mitigating pandemic risk. This underinvestment was widely acknowledged at the time, and occurred in spite of the well-established favorable cost-benefit ratios of such measures. In addition, international cooperation failed to lead to adequate preventive, mitigative and response policies. We argue that, considering the stakes, deepening our knowledge of why states do not adequately mitigate long-term destabilizing risks should be a priority for the study of governance. We conclude with recommendations both for policy makers and researchers. [ABSTRACT FROM AUTHOR]

To handle the risks related to coronavirus and the COVID-19 disease, governments worldwide have adopted different policies and strategies. These policies and strategies build on various approaches and methods to assess and convey the risks. This paper looks more closely into these approaches and methods. We review and discuss practices in four countries (Norway, the UK, the US and Sweden), focusing on the approaches, methods and models used to assess and describe the risks related to COVID-19. The main aims are to present some current thinking, reveal differences and suggest areas for improvement. The paper concludes that current practices can be enhanced by incorporating ideas and approaches from contemporary risk science, particularly in relation to how to treat uncertainties and reflect degrees of knowledge. [ABSTRACT FROM AUTHOR]

Human pressure on the environment is increasing the frequency, diversity, and spatial extent of disease outbreaks. Despite international recognition, the interconnection between the health of the environment, animals, and humans has been historically overlooked. Past and current initiatives have often neglected prevention under the One Health preparedness cycle, largely focusing on post-spillover stages. We argue that pandemic prevention initiatives have yet to produce actionable targets and indicators, connected to overarching goals, like it has been done for biodiversity loss and climate change. We show how the Driver-Pressure-State-Impact-Response framework, already employed by the Convention on Biological Diversity, can be repurposed to operationalize pandemic prevention. Global responses for pandemic prevention should strive for complementarity and synergies among initiatives, better articulating prevention under One Health. Without agreed-upon goals underpinning specific targets and interventions, current global efforts are unlikely to function at the speed and scale necessary to decrease the risk of disease outbreaks that might lead to pandemics. Threats to the environment are not always abatable, but decreasing the likelihood that environmental pressure leads to pandemics, and developing strategies to mitigate these impacts, are both attainable goals. [ABSTRACT FROM AUTHOR]

This article investigates the pandemic risk coverage within the European Union member states through insurance securitization. This strategy allows the transfer of health risks from the insurance market to the financial markets. We focus on the financial market crisis caused by the COVID-19 pandemic to securitize the losses caused by the latter. Over the period from 24/01/2020 (the first proven case of contamination in Europe) to 31/03/2020 (end of the dramatic decrease in financial markets), we apply the extreme value theory allowing the selection of the trigger threshold. We identify an immediate reaction of the financial markets following a pandemic shock, the effect of which fades after a few days. The response of stock market indices, measured by the fluctuation of return rates, is not very high. Nevertheless, the reaction of the financial markets is sufficient for the corona bond triggering, provided that the threshold for triggering the incidence rate is optimal. In addition, the securitization of insurance risk could be an alternative process to the classic risk transfer techniques such as co-insurance and reinsurance. Finally, a reinsurance pool dedicated to the insurance scheme's management against the effects of a pandemic is crucial for insurance securitization. These results could have implications for various actors such as insurers, financial investors, and States. [ABSTRACT FROM AUTHOR]

COVID-19 outbreaks at nursing homes during the recent pandemic have received ample media coverage and may have lasting negative impacts on individuals' perception of nursing homes. We argue that this could have sizable and persistent implications for savings and long-term care policies. Our theoretical model predicts that higher nursing home aversion should induce higher savings and stronger support for policies subsidizing home care. Based on a survey of Canadians aged 50 to 69, we document that higher nursing home aversion is widespread: 72% of respondents are less inclined to enter a nursing home because of the pandemic. Consistent with our model, we find that these respondents are more likely to have higher intended savings for old age because of the pandemic. We also find that they are more likely to strongly support home care subsidies. [ABSTRACT FROM AUTHOR]

Abstract Background Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. Methods A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. Results The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5–25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. Conclusions Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic. Graphical Abstract

Crude oil draws attention in recent research as its demand may indicate world economic growth trend in the post-COVID-19 era. In this paper, we study the dynamic lead–lag relationship between the COVID-19 pandemic and crude oil future prices. We perform rolling-sample tests to evidence whether two pandemic risk scores derived from network analysis, including a preparedness risk score and a severity risk score, Granger-cause changes in oil future prices. In our empirical analysis, we observe 49% to 60% of days in 2020 to 2021 during which the pandemic scores significantly affected oil futures. We also find an asymmetric lead–lag relationship, indicating that there is a tendency for oil futures to move significantly when the pandemic is less severe but not when it is more severe. This study adopts preparedness risk score and severity risk score as proxy variables to measure the impact of the COVID-19 pandemic risk on oil market. The asymmetric lead–lag behavior between pandemic risk and oil future prices provides insights on oil demand and consumption during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The aim of this article is to identify the risk and the likelihood of potential consequences of the COVID-19 pandemic on the business insurance market in the medium- and long-term horizon. The first section of this elaboration presents the theoretical approach connected with the nature of the pandemic. The second section outlines the presentation of the COVID-19 measurement rules and the third describes the potential effects of COVID-19 on the insurance market. Contrary to other elaborations on this topic which have appeared so far (these are mostly reports describing the financial market in short- and long-term horizons), here, the authors present the medium-term horizon as well. The possible consequences of COVID-19 are outlined both in relation to the insurance company client, e.g., change in the amount of the insurance premium under the insurance agreement, as well as in relation to the insurer, e.g., appearance of innovative and competitive offers (Trott's concept Special attention has been paid to the way in which the insurer's strategy (scenario analysis) may be used to build resilience to other crises as well as to the planning of emergency solutions. Actual events confirm the hypothesis that changes in the business insurance market dominated the losses in the aftermath of the pandemic. [ABSTRACT FROM AUTHOR]

Simon Grima,1 Murat Kizilkaya,2 Ramona Rupeika-Apoga,3 Inna Romānova,3 Rebecca Dalli Gonzi,4 Mihajlo Jakovljevic5– 7 1Department of Insurance, Faculty of Economics, Management and Accountancy, University of Malta, Msida, Malta; 2Department of Economics, Faculty of Economics and Administrative Sciences, Ardahan University, Ardahan, Turkey; 3Department of Business, Management and Economics, University of Latvia, Riga, Latvia; 4Department of Construction & Property Management, University of Malta, MSD, Msida, 2080, Malta; 5Institute of Comparative Economic Studies ICES, Faculty of Economics, Hosei University, Tokyo, Japan; 6Department of Global Health Economics and Policy, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia; 7Department of Public Health and Healthcare Named After N.A. Semashko, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, RussiaCorrespondence: Simon GrimaUniversity of Malta, Msida MSD 2080, MaltaTel +356 79 651 410Email simon.grima@um.edu.mtPurpose: The purpose of this study is to develop a Pandemic Risk Exposure Measurement (PREM) model to determine the factors that affect a country’s prospective vulnerability to a pandemic risk exposure also considering the current COVID-19 pandemic.Methods: To develop the model, drew up an inventory of possible factor variables that might expose a country’s vulnerability to a pandemic such as COVID-19. This model was based on the analysis of existing literature and consultations with some experts and associations. To support the inventory of selected possible factor variables, we have conducted a survey with participants sampled from people working in a risk management environment carrying out a risk management function. The data were subjected to statistical analysis, specifically exploratory factor analysis and Cronbach Alpha to determine and group these factor variables and determine their reliability, respectively. This enabled the development of the PREM model. To eliminate possible bias, hierarchical regression analysis was carried out to examine the effect of the “Level of Experienced Hazard of the Participant (LEH)” considering also the “Level of Expertise and Knowledge about Risk and Risk Management (LEK)”.Results: Exploratory factor analysis loaded best on four factors from 19 variables: Demographic Features, Country’s Activity Features, Economic Exposure and Societal Vulnerability (i.e. the PREM Model). This model explains 65.5% of the variance in the level of experienced hazard (LEH). Additionally, we determined that LEK explains only about 2% of the variance in LEH.Conclusion: The developed PREM model shows that monitoring of Demographic Features, Country’s Activity Features, Economic Exposure and Societal Vulnerability can help a country to identify the possible impact of pandemic risk exposure and develop policies, strategies, regulations, etc., to help a country strengthen its capacity to meet the economic, social and in turn healthcare demands due to pandemic hazards such as COVID-19.Keywords: COVID-19, pandemic risk, risk measurement model, hazard, exposure

Background: Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. Methods: A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. Results: The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5–25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. Conclusions: Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic. [ABSTRACT FROM AUTHOR]

In this editorial, we introduce a special thematic collection of articles published in this current issue, and earlier in 2021, which develop a range of social science approaches to studying and theorising pandemic risk, largely focused on the COVID-19 pandemic. We structure this editorial essay in two parts. First, we consider the challenges of theorising pandemics with an attentiveness to inequality. We consider what different theoretical approaches have brought and can bring to studying risk and inequality, before developing a more in-depth consideration of the work of Mary Douglas for this purpose. We draw out key features of Douglas's work on 'centre and periphery', alongside the related group dynamics and tensions which are configured by, and help reproduce, social inequalities. Second, we then develop a dialogue between these analytical sensibilities around inequality, drawn from Douglas, and various conceptualisations and findings emerging in the eight studies published on pandemics in Health, Risk and Society this year (4 in this issue, 4 appearing earlier in the year). Douglas's work, which has often been neglected in studies of inequalities and risk, provides valuable insights into institutional dynamics of culture and power. The eight recent studies in the journal include some conducted in contexts, and by researchers, located towards the global economic and academic periphery. This diversification, beyond the usual social and governmental contexts, and alongside the growing involvement of different epistemic communities, introduces and cultivates valuable insights, for the field of risk, inequality and health more generally, and for grasping the global phenomena of the pandemic. [ABSTRACT FROM AUTHOR]

The emergence of the novel COVID-19 has made pandemic preparedness a crucial issue for public health worldwide. A little is known about people behaviour in a pandemic situation. Such knowledge is however critical. Understanding the associated psychological and behavioural responses is vital for future intervention development. During the period of Lockdown Stage, from 25th March to 14th April 2020 in India, a survey was conducted among 700 people in Delhi NCR and Haryana state, to explore changes in their psychological and behavioural responses to the COVID-19 virus epidemic based on Protection Motivation Theory (PMT) which distinguishes two phases, namely a threat appraisal and a coping appraisal is applied . Further infected people should be isolated to minimize the spread of pandemic diseases. Therefore, the factors related to self-isolation (SI) should not be neglected, and it is important to investigate the factors leading the infected (or possibly infected) people to choose to self-isolate. For this purpose, the theory of planned behaviour (TPB) is used for providing conceptual framework for SI when facing a pandemic risk and a regression method is applied to investigate how attitude (ATT), subjective norms (SN), and perceived behavioural control (PBC) influence SI when facing a pandemic emergency. The results of this study provide relevant information that promotes theoretical understanding of psychological and behavioural intentions of people and enables us to shape future intervention development.

Abstract Background Avian influenza virus can directly cross species barriers and infect humans with high fatality. As antigen novelty for human host, the public health is being challenged seriously. The pandemic risk of avian influenza viruses should be analyzed and a prediction model should be constructed for virology applications. Results The 178 signature positions in 11 viral proteins were firstly screened as features by the scores of five amino acid factors and their random forest rankings. The Supporting Vector Machine algorithm achieved well performance. The most important amino acid factor (Factor 5) and the minimal range of signature positions (63 amino acid residues) were also explored. Moreover, human-origin avian influenza viruses with three or four genome segments from human virus had pandemic risk with high probability. Conclusion Using machine learning methods, the present paper scores the amino acid mutations and predicts pandemic risk with well performance. Although long evolution distances between avian and human viruses suggest that avian influenza virus in nature still need time to fix among human host, it should be notable that there are high pandemic risks for H7N9 and H9N2 avian viruses.

Background: Since 2011, influenza A viruses circulating in US swine exhibited at county fairs are associated with >460 zoonotic infections, presenting an ongoing pandemic risk. Swine "jackpot shows" that occur before county fairs each summer intermix large numbers of exhibition swine from diverse geographic locations. We investigated the role of jackpot shows in influenza zoonoses.Methods: We collected snout wipe or nasal swab samples from 17 009 pigs attending 350 national, state, and local swine exhibitions across 8 states during 2016-2018.Results: Influenza was detected in 13.9% of swine sampled at jackpot shows, and 76.3% of jackpot shows had at least 1 pig test positive. Jackpot shows had 4.3-fold higher odds of detecting at least 1 influenza-positive pig compared to county fairs. When influenza was detected at a county fair, almost half of pigs tested positive, clarifying why zoonotic infections occur primarily at county fairs.Conclusions: The earlier timing of jackpot shows and long-distance travel for repeated showing of individual pigs provide a pathway for the introduction of influenza into county fairs. Mitigation strategies aimed at curtailing influenza at jackpot shows are likely to have downstream effects on disease transmission at county fairs and zoonoses. [ABSTRACT FROM AUTHOR]

Global emergencies require coordinated and global responses: the COVID-19 world pandemic has made explicit the strong link of the current situation with the culture of risk. In particular, the spread of this disruptive virus and the possibility that the epidemic may will reach such a great extent in terms of increating demands and pressed capacity of patients in hospitals that it will no longer be manageable with current health facilities: therefore, this paper aims at investigating the potential of the synergic use of Risk Prevention and Monitoring System (RPMS) practices in the health sector, with the aim of improving the ability to predict and manage the risk of a pandemic, with a comprehensive Risk Management approach, in order to contain the adverse effects on the territory and therefore on the whole society. A literature review in the field of pandemic risk management was performed, with particular attention to the impact of the strategies and preventive models adopted in the health sector. In more details, organizational and managerial aspects, resources made available, advantages, enabling and hindering factors, and context of application were analysed, in order to grasp indications to promote the development of an integrated RPMS approach. The application of RPMS practices stands as the barrier of considerable social importance for the improvement of the quality and effectiveness of preventive health measures and involves the establishment of collaborative relationships among all the organizations involved, as well as the spread of a risk culture, with the final intent to become more resilient towards pandemic risk as COVID-19. [ABSTRACT FROM AUTHOR]

The aim of this article is to identify the risk and the likelihood of potential consequences of the COVID-19 pandemic on the business insurance market in the medium- and long-term horizon. The first section of this elaboration presents the theoretical approach connected with the nature of the pandemic. The second section outlines the presentation of the COVID-19 measurement rules and the third describes the potential effects of COVID-19 on the insurance market. Contrary to other elaborations on this topic which have appeared so far (these are mostly reports describing the financial market in short- and long-term horizons), here, the authors present the medium-term horizon as well. The possible consequences of COVID-19 are outlined both in relation to the insurance company client, e.g., change in the amount of the insurance premium under the insurance agreement, as well as in relation to the insurer, e.g., appearance of innovative and competitive offers (Trott’s concept Special attention has been paid to the way in which the insurer’s strategy (scenario analysis) may be used to build resilience to other crises as well as to the planning of emergency solutions. Actual events confirm the hypothesis that changes in the business insurance market dominated the losses in the aftermath of the pandemic.

COVID‐19 has proven that pandemic risk deems to the type of catastrophe risk that needs to be treated seriously, by both society and the insurance industry. A key element to measure, manage, and transfer pandemic risk is the modeling capability. This paper first reviews the insured loss from COVID‐19 and the impact on the insurance industry. Then, current pandemic risk modeling capabilities and how insurance industry uses these models are evaluated. Some suggestions are made in terms of how these models can be improved in the future and how they can assist in insuring the pandemic risk. Finally, the nonmodeling elements of pandemic risk transfer and the government's role are discussed. [ABSTRACT FROM AUTHOR]

This paper analyzes the insurability of pandemic risk and outlines how underwriting policies and scenario analysis are used to build resilience upfront and plan contingency actions for crisis scenarios. It then summarizes the unique "lessons learned" from the Covid-19 crisis by baselining actual developments against a reasonable, pre-Covid-19 pandemic scenario based on the 2002 SARS epidemic and 1918 Spanish influenza pandemic. Actual developments support the pre-Covid-19 hypothesis that financial market developments dominate claims losses due to the demographics of pandemics and other factors. However, Covid-19 "surprised" relative to the pre-Covid-19 scenario in terms of its impact on the real economy as well as on the property and casualty segment as business interruption property triggers and exclusions are challenged, something that may adversely impact the insurability of pandemics as well as the perception of the industry for some time to come. The unique lessons of Covid-19 reinforce the need for resilience upfront in solvency and liquidity, the need to improve business interruption wordings and re-underwrite the book, and the recognition that business interruption caused by pandemics may not be an insurable risk due to its large accumulation potential and the threat of external moral hazard. These insurability limitations lead to a discussion about the structure and financing of protection against the impact of future pandemics. [ABSTRACT FROM AUTHOR]

Introduction: Two species of Aedes (Ae.) mosquitoes (Ae. aegypti and Ae. albopictus) are primary vectors for emerging arboviruses that are a significant threat to public health and economic burden worldwide. Distribution of these vectors and the associated arboviruses, such as dengue virus, chikungunya virus, yellow fever virus, and Zika virus, was for a long time restricted by geographical, ecological, and biological factors. Presently, arbovirus emergence and dispersion are more rapid and geographically widespread, largely due to expansion of the range for these two mosquitoes that have exploited the global transportation network, land perturbation, and failure to contain the mosquito population coupled with enhanced vector competence. Ae. aegypti and Ae. albopictus may also sustain transmission between humans without having to depend on their natural reservoir forest cycles due to arthropod adaptation to urbanization. Currently, there is no single strategy that is adequate to control these vectors, especially when managing arbovirus outbreaks. Objective: This review aimed at presenting the characteristics and abilities of Ae. aegypti and Ae. albopictus, which can drive a global public health risk, and suggests strategies for prevention and control. Methods: This review presents the geographic range, reproduction and ecology, vector competence, genetic evolution, and biological and chemical control of these two mosquito species and how they have changed and developed over time combined with factors that may drive pandemics and mitigation measures. Conclusion: We suggest that more efforts should be geared toward the development of a concerted multidisciplinary approach. [ABSTRACT FROM AUTHOR]

Background: Avian influenza virus can directly cross species barriers and infect humans with high fatality. As antigen novelty for human host, the public health is being challenged seriously. The pandemic risk of avian influenza viruses should be analyzed and a prediction model should be constructed for virology applications. Results: The 178 signature positions in 11 viral proteins were firstly screened as features by the scores of five amino acid factors and their random forest rankings. The Supporting Vector Machine algorithm achieved well performance. The most important amino acid factor (Factor 5) and the minimal range of signature positions (63 amino acid residues) were also explored. Moreover, human-origin avian influenza viruses with three or four genome segments from human virus had pandemic risk with high probability. Conclusion: Using machine learning methods, the present paper scores the amino acid mutations and predicts pandemic risk with well performance. Although long evolution distances between avian and human viruses suggest that avian influenza virus in nature still need time to fix among human host, it should be notable that there are high pandemic risks for H7N9 and H9N2 avian viruses. [ABSTRACT FROM AUTHOR]

Global swine populations infected with influenza A viruses pose a persistent pandemic risk. With the exception of a few countries, our understanding of the genetic diversity of swine influenza viruses is limited, hampering control measures and pandemic risk assessment. Here we report the genomic characteristics and evolutionary history of influenza A viruses isolated in Australia from 2012 to 2016 from two geographically isolated swine populations in the states of Queensland and Western Australia. Phylogenetic analysis with an expansive human and swine influenza virus data set comprising >40,000 sequences sampled globally revealed evidence of the pervasive introduction and long-term establishment of gene segments derived from several human influenza viruses of past seasons, including the H1N1/ 1977, H1N1/1995, H3N2/1968, and H3N2/2003, and the H1N1 2009 pandemic (H1N1pdm09) influenza A viruses, and a genotype that contained gene segments derived from the past three pandemics (1968, reemerged 1977, and 2009). Of the six human-derived gene lineages, only one, comprising two viruses isolated in Queensland during 2012, was closely related to swine viruses detected from other regions, indicating a previously undetected circulation of Australian swine lineages for approximately 3 to 44 years. Although the date of introduction of these lineages into Australian swine populations could not be accurately ascertained, we found evidence of sustained transmission of two lineages in swine from 2012 to 2016. The continued detection of human-origin influenza virus lineages in swine over several decades with little or unpredictable antigenic drift indicates that isolated swine populations can act as antigenic archives of human influenza viruses, raising the risk of reemergence in humans when sufficient susceptible populations arise. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks., Objective: This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk., Methods: We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated., Results: Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores., Conclusions: The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet., (©Amanda MY Chu, Andy C Y Chong, Nick H T Lai, Agnes Tiwari, Mike K P So. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 07.09.2023.)